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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 3826–3850 of 9051 papers

TitleStatusHype
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data—0
Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems—0
Natural Language Generation for Advertising: A Survey—0
Toward Leveraging Pre-Trained Self-Supervised Frontends for Automatic Singing Voice Understanding Tasks: Three Case Studies—0
On Evaluation of Document Classification using RVL-CDIP—0
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event—0
From structure mining to unsupervised exploration of atomic octahedral networksCode0
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast—0
RSMT: Real-time Stylized Motion Transition for CharactersCode1
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation—0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
Spatial Heterophily Aware Graph Neural NetworksCode0
grenedalf: population genetic statistics for the next generation of pool sequencingCode1
Explicit Syntactic Guidance for Neural Text GenerationCode1
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation—0
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Lingua Manga: A Generic Large Language Model Centric System for Data Curation—0
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
Optimal Execution Using Reinforcement Learning—0
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Perturbation-Based Two-Stage Multi-Domain Active Learning—0
Chaotic turnover of rare and abundant species in a strongly interacting model community—0
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target ImputationCode0
Conditional Text Image Generation with Diffusion Models—0
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